Electrical Impedance Mammography

Abstract

An example relates to a method for virtually refining an experimental electrical impedance signal for a target tissue using a non-target tissue model. The method includes obtaining a refined impedance transfer function based on an electrical impedance model that includes one or more non-target tissue components but does not include the target tissue component, and refining the experimental electrical impedance signal using the refined impedance transfer function to obtain a virtually refined signal for the target tissue component. The method includes accessing an impedance transfer function that is dependent on the non-tumor tissue according to age and / or degree of obesity, and modifying the experimental electrical impedance signal using the obtained impedance transfer function to obtain a signal that is less dependent on the non-tumor tissue and more dependent on the impedance of the tumor tissue.

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE Embodiments of the present disclosure relate to electrical impedance spectroscopy, electrical impedance imaging, and electrical impedance mammography.

[0002] Embodiments of the present invention relate to devices, computer programs, and methods for electrical impedance imaging, and in particular to devices, computer programs, and methods for electrical impedance imaging of a female breast to facilitate detection of changes within the breast mass, including changes such as tumors, abnormalities, malignant changes such as cancer, within the breast. [Background technology]

[0003] Electrical impedance mammography (EIM), or electrical impedance imaging (Ell), also known as electrical impedance tomography (EIT), electrical impedance scanner (EIS), and applied potential tomography (APT), is an imaging technique used especially in medical applications.

[0004] This technique images the spatial distribution of electrical impedance within an object, such as the human body, and is attractive as a medical diagnostic tool because it is non-invasive and does not require the use of ionizing radiation, as in X-ray tomography, or the generation of strong, highly uniform magnetic fields, as in magnetic resonance imaging (MRI).

[0005] Typically, a two-dimensional (2D) or three-dimensional (3D) array of evenly spaced electrodes is attached to the object being imaged around a region of interest (ROI). An input voltage is applied to a pair of "input" electrodes and an output current is measured at the "output" electrodes, or an input current is applied between a pair of "input" electrodes and an output voltage is measured between the "output" electrodes or between a pair of output electrodes. For example, a very small alternating current is passed between a pair of "input" electrodes, and the potential difference between all other "output" electrodes is measured. A current is then passed between a different pair of "input" electrodes, and the potential difference between all other pairs of "output" electrodes is measured. An image is constructed using appropriate image reconstruction techniques.

[0006] Spatial variations revealed in electrical impedance images can arise from changes in impedance between healthy and non-healthy tissue, changes in impedance between different tissues or organs, or changes in apparent impedance due to anisotropic effects arising from muscle configuration, etc.

[0007] Cancer-related tissue or cellular changes cause significant local changes in electrical impedance that can be imaged. WO 00 / 12005 discloses an example of an electrical impedance imaging device that can be used to detect breast cancer or other cancers. Summary of the Invention [Problem to be solved by the invention]

[0008] The changes in the individual impedance characteristics can be used to analyze the structure of an object, for example in the case of human tissue, variations in the individual impedance characteristics can indicate the presence of abnormalities, as they result in electrical characteristics that are different from those of normal, healthy tissue at the macro level (millimeter range) or that are indicative of special cell groups at the micro level (micrometer range), which can lead to early abnormal changes, especially those of malignant tumors such as cancer.

[0009] However, the amount of change in individual impedance characteristics at the macro- or micro-range level may be insufficient to accurately analyze tissue / cell structures or abnormalities by in vitro or in vivo-based EIM techniques. For example, the amount of change in the capacitance (C) or relaxation frequency (fr) of a cell membrane may be insufficient to be easily detected in an image of an object constructed based on its individual impedance characteristics.

[0010] Therefore, it has the advantage of virtually refining the signal before processing and identifying abnormal changes, including benign and malignant lesions.

[0011] Therefore, by ensuring that the signal is determined primarily by changes in impedance of the target tissue and not other tissues, it has the advantage of being able to distinguish between benign and malignant changes. [Means for solving the problem]

[0012] In accordance with various embodiments, some, but not all, examples set forth in the accompanying claims are provided. [Effects of the Invention]

[0013] This is the first time that a histopathologically derived in vivo impedance model has been used to refine and identify cell-based abnormal tissues in 2D and 3D EIM imaging using a post-tissue refinement in vitro database.

[0014] Some examples will now be described with reference to the accompanying drawings.

[0015] FIG. 1 shows an example of the subject matter described herein. FIG. 2 shows an example of the subject matter described herein. FIG. 3 shows an example of the subject matter described herein. FIG. 4 shows an example of the subject matter described herein. FIG. 5 shows an example of the subject matter described herein. FIG. 6 shows an example of the subject matter described herein. [Brief explanation of the drawings]

[0016] [Figure 1] FIG. 1 is a schematic diagram of an electrical impedance tomography device. [Figure 2] FIG. 2 shows a graph of the measured electrical impedance as a function of frequency for single or multiple dispersion. [Figure 3] 3A and 3B show an example of an electrical impedance circuit model of an object. [Figure 4] Figure 4 shows the flow chart. [Figure 5]Figure 5 shows the controller. [Figure 6] FIG. 6 shows the computer program. DETAILED DESCRIPTION OF THE INVENTION

[0017] FIG. 1 diagrammatically illustrates an electrical impedance measurement device or electrical impedance tomography (EIT) device 10 for measuring impedance data of a load 12. The load 12 comprises a conductive object having a plurality of electrodes attached thereto. The term "conductive" means that the object is capable of conducting electric current, but does not necessarily conduct the current very well. The device 10 further includes a signal source 14, a signal detector 16, and a computer 18. In one embodiment, the signal source provides a current as an input signal, and the signal detector detects a voltage as an output signal. In another embodiment, the signal source provides a voltage as an input signal, and the signal detector detects a current as an output signal.

[0018] A computer typically includes at least a processor and a memory, which stores computer programs that, when loaded into the processor, control the computer.

[0019] An input signal is applied to the subject via an electrode using source 14, and the resulting output signal present at the same or other electrode is measured using detector 16. This process is repeated for different frequencies of the input signal. For example, electrical signals at various frequencies from 0 Hz (DC) to 100 MHz may be applied by signal source 14, allowing frequency-dependent electrical impedance data of the subject to be obtained.

[0020] The spacing of the electrodes used for impedance measurements determines the resolution or scale at which the object is analyzed. Electrical impedance measurements can be obtained at the expected scale of interest (e.g., in the micrometer or millimeter range). Examples of scales of interest include, for biological objects, the interest may be at the single cell, group cell level, or tissue or histology level, such as lobules or ducts in breast tissue. The obtained electrical impedance data is then analyzed using the transfer function of an assumed electrical model to determine multiple electrical impedance properties of the object. The electrical model used may vary depending on the resolution / scale of the impedance measurement.

[0021] Referring to Figure 2, electrical impedance data obtained using the above method can be plotted as a function of frequency. This plot 22 represents the change in impedance of the object versus frequency, or transfer function. The computer 18 is operable to execute a suitable algorithm to analyze the obtained impedance transfer function or frequency-dependent impedance characteristics, thereby determining a number of electrical impedance characteristics of the object. The electrical impedance characteristics typically include one or more of the following: a) Impedance at the limit ω->0 (lower limit) b) Impedance at the limit ω->∞ (upper limit) c) (i) Relaxation frequencies at which changes occur in the impedance properties of tissue structures, membranes, or cells. (ii) Impedance at the frequency of change (iii) The gradient of the impedance change, especially at the relaxation frequency, which is related to the properties of the cell membrane and the distribution of the diversity of the cell membrane population.

[0022] For example, if there are N dispersions (N>1) including alpha, beta, and gamma dispersions of biological substances [Cole K S, Permeability and impermeability of cell membranes for ions. Cold Spring Harbor Symp. Quant. Biol. 8 pp110-22, 1940] within the frequency range used, the dispersion frequencies ω1, ω2, … ω N-1 , ω N are identified, and the electrical impedance characteristics of a specific dispersion m usually include one or more of the following: a) When m = 1, the impedance at the lower (overall) limit ω->0 When m>1, the impedance at the lower (local) limit is ω->ω m -a, where a < (ω m -ω m-1 ), and it can be 1 / 2(ω m -ω m-i ). b) When m = N, the impedance at the upper (overall) limit is ω->∞ When m<N, the impedance at the upper (local) limit is ω->ω m +b, where b < (ω m+i -ω m ), and b ~ 1 / 2(ω m+i -ω m ). c) (i) The relaxation frequency ω m (f rm ), at which the impedance changes (ii) The impedance at that change frequency (iii) The gradient of the change

[0023] Using the change amount of one or more of these impedance characteristics, the structure of an object can be analyzed by the intracellular / extracellular or intracellular / extracellular similar related changes in the pathological characteristics at the macro or micro range level.

[0024] In some embodiments, the object being analyzed is modeled using an equivalent electrical impedance circuit. The object can be modeled using equivalent electrical impedance circuit 20 shown in FIG. 3. Objects that can be modeled using equivalent electrical impedance circuit 20 include, by way of non-limiting example, cascade structures of human or animal tissue, porous or other cells or cell-like materials, e.g., Z1, Z2, and Z3 each including a similar triplet (e.g., Z1-1, Z1-2, Z1-3, etc., to represent a single cell, a small group of cells in the micro range, or a large group of cells at the macro or tissue level).

[0025] In the illustrated embodiment, the equivalent electrical impedance circuit 20 includes a cellular portion 21 in parallel with an extracellular portion 23. The cellular portion 21 has a capacitance C and a resistance R i The resistance C is associated with the cell membrane / boundary and the resistance R i is related to the inside of the cell. The extracellular part 23 is the resistance R e Resistance R e is associated with structures outside the cell and has a capacitance C and a resistance R connected in series. i is connected in parallel with

[0026] A non-limiting example of a single distributed impedance transfer function for this circuit is:

number

[0027] There is a change (dispersion) in the impedance Zr with frequency fr and slope a.

[0028] The propagation of multiple dispersions in biological tissues can be modeled by the Cole-Cole equation (Cole KS 1940, Cole KS 1941, McAdams ET et al., 1995) as follows:

number

number

[0029] frequency f ri and gradient α i Impedance Z with ri There is a change (variance) in

[0030] As previously mentioned, the computer 18 is operable to execute a suitable algorithm to analyze the measured impedance data and extract multiple electrical impedance characteristics of the object being analyzed. For example, based on the measured impedance data, the algorithm is operable to plot the impedance data points as a function of frequency and use a model to generate a best fit line 22 to form the transfer function shown in FIG. 2. From this transfer function, the computer 18 can determine multiple individual impedance characteristics of the object. These impedance characteristics include: a) The impedance at the limit ω->0 is R e become b) The impedance at the limit ω->∞ is R i R e / (R i +R e ) c) (i) The relaxation frequency f at which the impedance changes r (ii) Impedance Z of the transfer function at the change frequency r (iii) The gradient of change giving the relaxation coefficient a.

[0031] The impedance characteristics can be used to determine further impedance characteristics using a model.

[0032] For example, R e and R i R e / (R i +R e ), if both are known, then R i can be determined.

[0033] Variation (dispersion) frequency f r Impedance Z of the transfer function at r is the part where the capacitor dominates the transfer characteristics, because with each small increase in frequency, the conductivity improves significantly and the impedance decreases. r. Impedance Z at r is 1 / (j.2πf r. C), so C is 1 / (j.2πf r Z r ) can be determined as

[0034] Individual impedance characteristics (R e , R i , f r , Z r The variations in impedances (a, c) can be used to analyze the structure of an object at a specific level, either macro or microscopic. For example, in the case of human tissue, variations in the individual impedance characteristics can indicate the presence of an abnormality, since they result in electrical properties that differ from those exhibited by normal, healthy tissue.

[0035] However, the amount of change in individual impedance characteristics may be insufficient to allow accurate analysis of the structure. For example, the capacitance (C) or relaxation frequency (f) of a cell membrane may not be accurately determined. r) may be insufficient to be easily detected, for example, in an image of an object constructed based on its individual impedance characteristics.

[0036] 3A shows a mixed impedance model of the object under analysis to represent in various ways the reality of mixed tissues including normal stoma, glandular tissue, or abnormal tissue growth. In the illustrated embodiment, an equivalent electrical impedance circuit 30A includes a containing portion 31 in parallel with a mutually containing portion 33. The containing portion 31 connects impedances Z1 and Z3 in series. The mutually containing portion 33 has an impedance Z3. Impedance Z3 is associated with structures outside the interstitial portion. Impedance Z3 is connected in parallel with the series-connected impedances Z1 and Z2.

[0037] The impedance transfer function of this circuit 30A is:

number

[0038] 3B shows a mixed impedance model of the analyte in the limit where the impedance of the target tissue is zero. In the illustrated embodiment, the equivalent electrical impedance circuit 30b includes impedance Z3 in parallel with impedance Z2.

[0039] The impedance transfer function of this circuit 30B is:

number

number

[0040] S is the experimental data (frequency domain), and Z A is a model transfer function including the target and non-target tissues. It is based on a circuit model including the electrical impedance of the target tissue and the electrical impedance of the non-target tissues (e.g., Z1, Z2, Z3). Z B is the model transfer function of the non-target tissues based on the same circuit model but including the electrical impedances of the non-target tissues (Z2, Z3, etc.) and not the electrical impedance of the target tissue Z1. N is the noise signal, which is the signal originating from non-target tissue. C is the signal of interest (the experimental signal S with noise N removed).

[0041] S=Z A *s ⇒s=Z A -1 *s (formula 1) N=Z B *s Substituting Equation 1, we get N=Z B *Z A -1 *S C=SN=SZ B *Z A -1 *S Therefore, C is S and Z B , Z A can be determined from knowledge of

[0042] S(t) is the experimental data (time domain). S is the experimental data (frequency domain). Z A is a model transfer function that includes the target and non-target tissues and is based on a circuit model that includes the electrical impedance of the target tissue and the electrical impedance of the non-target tissues (e.g., Z1, Z2, Z3). Z B is the model transfer function of the non-target tissues based on the same circuit model but including the electrical impedances of the non-target tissues (Z2, Z3, etc.) and not the electrical impedance of the target tissue Z1. N is the noise signal, which is the signal originating from non-target tissue in the frequency domain, and N(t) is the equivalent signal in the time domain. C is the signal of interest (the experimental signal S with noise N removed) in the frequency domain, and C(t) is the equivalent signal in the time-space domain.

[0043] S=Z A *s ⇒S=Z A -1 *s (formula 1) N=Z B *s Substituting Equation 1, we get N=Z B *Z A -1 *S N(t)=T -1 {Z B *Z A -1 *T{S(t)}} C(t)=S(t)-N(t)=S(t)-T -1 {Z B *Z A -1 *T{S(t)}} where T{} is the forward transform from time space to frequency, and T -1 {} is the inverse transform from frequency to time space. Therefore, C(t) is the product of S(t) and Z B , Z A can be determined from knowledge of

[0044] Figure 4 shows how a non-target tissue model (such as Figure 3B) can be used to virtually refine an experimental electrical impedance signal for a target tissue. In some examples, the target tissue is tumor tissue.

[0045] The illustrated method is: Obtaining a refined impedance transfer function (ZB) based on an electrical impedance model (Figure 3B) that includes one or more non-target tissue components (Z2, Z3) but does not include the target tissue component (Z1); It involves refining the experimental electrical impedance signal (S, S(t)) using a refined impedance transfer function (ZB) to obtain a virtually refined signal (C, C(t)) of the target tissue constituents.

[0046] Refining the experimental electrical impedance signal (S, S(t)) using a refined impedance transfer function (ZB) to obtain a virtually refined signal of the target tissue component can be performed by assuming an electrical impedance model (Figure 3A) that includes the target tissue component and one or more non-target tissue components, where the target tissue is the tumor tissue and the non-target tissue is the non-tumor tissue.

[0047] The transfer function (Z A ) into the transfer function (Z B ), virtually refined signals (C, C(t)) of target tissue components can be obtained.

[0048] The transfer function (Z A ) is based on target tissue impedance Z1 and non-target tissue impedances Z2 and Z3. The reduced electrical impedance model (Figure 3B) is based on non-target tissue impedances Z2 and Z3.

[0049] The non-target tissue impedances Z2, Z3 may be based on the baseline impedance values ​​of pure non-target tissue modified by an optimization factor (R).

[0050] The optimization coefficient (R) is correlated with one or more characteristics of the patient, such as age and / or obesity (varying BMI).

[0051] Assigning an appropriate optimization factor (R) to a subject results in a classification problem that can be addressed using machine learning.

[0052] In at least some instances, this method: Accessing the impedance transfer function ZB that depends on non-tumor tissue (e.g., depends on Z2, Z3, R) as a function of age and / or obesity (or BMI); The resulting impedance transfer function Z B This involves correcting the experimental electrical impedance signal using to obtain a signal that is less dependent on the impedance of non-tumor tissue (Z1, Z2) and more dependent on the impedance of tumor tissue (Z1).

[0053] One indicator of obesity is body mass index.

[0054] The non-tumor tissue impedance can be based on connective tissue impedance and adipose tissue impedance.

[0055] Depending on the subject, tumor tissue impedance may be benign, such as fibroadenoma tissue impedance, or malignant, such as ductal carcinoma tissue, lobular carcinoma tissue, or ductal carcinoma in situ (DCIS) impedance.

[0056] The experimental signal is an electrical impedance signal from an in vitro measurement, which can be used to analyze an in vitro sample.

[0057] The experimental signal is an electrical impedance signal from an in vivo measurement and can be used for in vivo analysis.

[0058] Electrical impedance signals can be acquired from 2D or 3D regions of interest (ROIs) on EIM images with known tissue regions in the ROI based on a tissue impedance database or the measured BMI of a particular patient volunteer.

[0059] The electrical impedance signal can be used for electrical impedance imaging.

[0060] In at least some examples, the electrical impedance model is a mixed impedance model that includes a target tissue impedance (Z1) and non-target tissue impedances (Z2, Z3).

[0061] The target tissue impedance Z1 and a first non-target tissue impedance Z3 are electrically in series as a combination, and the combination is electrically in parallel with a second non-target tissue impedance Z2 (see FIG. 3A).

[0062] The target tissue impedance Z1 will be either a fibroadenoma reference impedance or an induced cancer reference impedance depending on the desired target.

[0063] The first non-target tissue impedance Z3 is one of the connective tissue impedance and the adipose tissue impedance, and the second non-target tissue impedance Z2 is the other of the connective tissue impedance and the adipose tissue impedance.

[0064] This method can use an in vitro sample to determine the target tissue reference impedance (Z1) and an in vitro sample to determine the non-target tissue reference impedance. The in vitro sample can be used to determine the first non-target tissue reference impedance, Z3. The in vitro sample can be used to determine the second non-target tissue reference impedance, Z2.

[0065] The electrical impedance model can be determined by best fitting to the in vitro data of one or more samples. For example, the relative ratio R between the first non-target tissue reference impedance Z3 and the second non-target tissue reference impedance R of the electrical impedance model can be determined by optimization (best fit) when the target tissue impedance (Z1) is known.

[0066] The electrical impedance model (FIG. 3A) is converted to a non-target model (FIG. 3B) for non-target tissue by setting the impedance of the target tissue (Z1) to zero in the electrical impedance model (FIG. 3A).

[0067] A non-target tissue model (Figure 3B) is used to estimate the non-target components, which are then subtracted from the experimental electrical impedance signal to estimate the target signal. The subtraction can be performed in the time domain or the frequency domain.

[0068] The non-target tissue model (Figure 3B) is used to estimate the non-target signal, which is subtracted from the experimental signal to estimate the target signal.

[0069] In vivo target signals can be analyzed to identify and locate spatial electrical impedance changes representative of tumor tissue.

[0070] The in vivo target signal can be analyzed to image tumor tissue, such as with 2D or 3D EIM images.

[0071] In some examples, the computer program 406 comprises computer code that, when executed by one or more processors, performs one or more of the described methods.

[0072] In some examples, the imaging system includes a controller or other means for performing one or more of the described methods.

[0073] Referring again to FIG.

[0074] In blocks 42, 44, and 46: Use in vitro samples to determine the impedance parameter Z1 of a mixed model (Z1, Z2, Z3) containing target tissue (Z1) and non-target tissues (Z2, Z3).

[0075] Obtain Z scores for connective tissue, adipose tissue, fibroadenoma, ductal carcinoma, lobular carcinoma, and DCIS.

[0076] The impedance of the target tissue (fibroadenoma, ductal carcinoma, lobular carcinoma, DCIS) is used as Z1 in the modeling process.

[0077] Z2 and Z3 are connective tissue and adipose tissue, and their relative proportions are determined by optimization (best fit).

[0078] In block 42: Collect EIS data for the following types of organizations:

[0079] [Table 1]

[0080] Blocks 44, 46: Determine Z1 as IDC or fibroadenoma, and Z2 and Z3 as adipose tissue or connective tissue.

[0081] Blocks 48, 50, 52: For a particular sample, the associated mixture model (Z1, Z2, Z3) is determined by the best fit to the sample's in vitro data (find Z2, Z3 of the model, Z1 is known). For example, find the optimized parameter R.

[0082] Examination of images on histopathology slides is used to verify how reliable this "best fit" ratio R is.

[0083] In step 54: The optimized mixed model (Z1, Z2, Z3, e.g., Figure 3A) is converted into a non-target model (Z2, Z3, Z1->0, e.g., Figure 3B) for non-target tissues.

[0084] In block 56: Non-target tissue models (Z2, Z3, e.g., Figure 3B) are used to estimate the non-target signal, which is subtracted from the experimental signal to estimate the target signal (Z1).

[0085] The experimental signal may be an in vitro signal 43 or a region of interest (ROI) of an in vivo signal 55 .

[0086] Analysis of in vitro impedance data 43 to identify the presence of malignancies such as IDC, where the experimental signal is associated with a particular sample, and spatial variations in the impedance of the target signal are indicative of IDC within the sample.

[0087] Analysis of in vivo data 55 to identify and locate spatial variations ΔZ indicative of cancer or other target tissue. In this case, the experimental signal is an in vivo signal 55. Spatial variations in the impedance of the target signal are indicative of cancer in vivo.

[0088] Method blocks 40 through 54 are repeated to obtain suitable non-target tissue models for different combinations of BMI and age. The tissue composition will be similar in a particular sample and in vivo.

[0089] The ratio R of Z2 and Z3 can be statistically classified into different age or BMI groups. That is, Z1 is the reference impedance and does not change in any classification, but the values ​​of Z2 and Z3 have different ranges identified by R when classified into different ages or BMIs, for example. Non-target tissue models (Figure 3B) can be searched for age / BMI or other classification parameters to make them suitable for use with experimental in vivo signals.

[0090] The non-target tissue model is then used in block 56 to obtain a better data signal 57 for subsequent analysis (eg, imaging).

[0091] 5 shows an example of a controller 400. The implementation of the controller 400 can be as a controller circuit. The controller 400 can be implemented solely in hardware, have certain aspects in software that include firmware only, or be a combination of hardware and software (including firmware).

[0092] 5, the controller 400 may be implemented using instructions that enable hardware functionality, for example, executable instructions of a computer program 406 in a general-purpose or special-purpose processor 402. The computer program 406 is stored on a computer-readable storage medium (disk, memory, etc.) and executed by such processor 402.

[0093] The processor 402 is configured to read from and write to the memory 404. The processor 402 may also include an output interface through which data and / or commands are output by the processor 402, and an input interface through which data and / or commands are input to the processor 402.

[0094] The memory 404 stores a computer program 406 that includes computer program instructions (computer program code) that, when loaded into the processor 402, control the operation of the device 10. The computer program instructions in the computer program 406 provide the logic and routines that enable the device to perform the methods illustrated in the figures. The processor 402 can load and execute the computer program 406 by reading the memory 404.

[0095] Thus, the device 10: at least one processor 402; and At least one memory 404 containing computer program code , at least one memory 404 and computer program code configured to, together with at least one processor 402, cause the device 10 to perform at least one or more of the methods described.

[0096] 6, the computer program 406 may arrive at the device 10 via any suitable distribution mechanism 408. The distribution mechanism 408 may be, for example, a machine-readable medium, a computer-readable medium, a non-transitory computer-readable storage medium, a computer program product, a memory device, a recording medium such as a compact disc read-only memory (CD-ROM) or a digital versatile disc (DVD), or a solid-state memory, an article of manufacture that contains or tangibly embodies the computer program 406, etc. The distribution mechanism may also be a signal configured to reliably transfer the computer program 406. The device 10 may propagate or transmit the computer program 406 as a computer data signal.

[0097] Computer program instructions for causing an apparatus to perform at least the following or to perform one or more of the described methods:

[0098] The computer program instructions may be included in a computer program, a non-transitory computer-readable medium, a computer program product, a machine-readable medium, etc. In some, but not necessarily all, examples, the computer program instructions may be distributed across multiple computer programs.

[0099] Although memory 404 is shown as a single component / circuit, it may also be implemented as one or more separate components / circuits, some or all of which may be integrated / removable and / or provide persistent / semi-persistent / dynamic / cached storage.

[0100] Although the processor 402 is shown as a single component / circuit, it may also be implemented as one or more separate components / circuits, some or all of which may be integrated / removable. The processor 402 may be a single-core processor or a multi-core processor.

[0101] References to "computer-readable storage medium," "computer program product," "tangibly embodied computer program," etc., or to "controller," "computer," "processor," etc., should be understood to include computers having different architectures, such as single / multi-processor architectures and sequential (von Neumann) / parallel architectures, as well as specialized circuitry, such as field programmable gate arrays (FPGAs), application specific circuits (ASICs), signal processing devices, and other processing circuitry. References to computer programs, instructions, code, etc., should be understood to include software for a programmable processor, or firmware, e.g., the programmable content of a hardware device, e.g., instructions for a processor, or configuration settings for a fixed function device, gate array, or programmable logic device.

[0102] The blocks illustrated in the figures may represent method steps and / or sections of code of the computer program 406. The illustration of a particular order of the blocks does not necessarily imply a required or preferred order for the blocks, and the order and arrangement of the blocks may be changed. Additionally, some blocks may be omitted.

[0103] Where a structural feature is recited, that feature may be replaced by a means that performs one or more of the functions of that structural feature, whether that function is explicitly or implicitly recited.

[0104] Systems, devices, methods, and computer programs may use machine learning, including statistical learning. Machine learning is a field of computer science that empowers computers with the ability to learn without being explicitly programmed. A computer learns from experience E for some class of tasks T and performance measure P if experience E improves performance on task T, as measured by P. Computers can often learn from past training data to predict future data. Machine learning includes fully or partially supervised learning and fully or partially unsupervised learning. Discrete outputs (e.g., classification, clustering) and continuous outputs (e.g., regression) are possible. Machine learning can be implemented using various approaches, such as cost function minimization, artificial neural networks, support vector machines, and Bayesian networks. Cost function minimization can be used, for example, in linear regression, polynomial regression, and k-means clustering. Artificial neural networks, for example, with one or more hidden layers, model complex relationships between input and output vectors. Support vector machines can be used for supervised learning. A Bayesian network is a directed acyclic graph that represents the conditional independence of many random variables.

[0105] As used herein, the term "comprises" is used in an inclusive rather than exclusive sense, i.e., a reference to X comprising Y indicates that X may include only one Y or may include multiple Ys. When "comprises" is used in an exclusive sense, this will be clarified in the context by reference to "including only one of" or "consisting of."

[0106] In this description, various examples are referenced. The description of a feature or function in connection with an example indicates that the feature or function is present in that example. The use of the terms "example," "for example," "may," or "might" in this text, whether explicitly stated or not, means that such feature or function is present in at least the described example, whether or not it is described as an example, and may, but not necessarily, be present in some or all of the other examples. Thus, "example," "for example," "may," or "may" refers to a particular instance within a class of examples. A property of an instance may be a property of that instance only, a property of the class, or a property of a subclass of the class that includes some but not all instances within the class. Thus, a feature described with reference to one example but not with reference to another example is implicitly disclosed as being usable, where possible, as part of a functional combination in other examples, but not necessarily in the other examples.

[0107] Although the examples have been described in the preceding paragraphs with reference to various examples, it should be understood that changes can be made to the examples shown without departing from the scope of the claims.

[0108] Features set out in the foregoing description may be used in combinations other than those expressly set out above.

[0109] Although functions are described with reference to particular features, those functions may be performed by other features whether or not described.

[0110] Although features are described with reference to particular examples, those features may be present in other examples whether or not they are described.

[0111] As used herein, the terms "a" or "the" are used in an inclusive rather than exclusive sense. That is, a reference to X including Y may refer to X including only one Y or to multiple Ys, unless the context clearly indicates otherwise. When "a" or "the" is used in an exclusive sense, the context will make this clear. In some situations, "at least one" or "one or more" may be used to emphasize an inclusive sense, but the absence of these terms should not be construed as an exclusive sense.

[0112] The presence of a feature (or combination of features) in a claim is a reference both to the feature or (combination of features) itself and to features that achieve substantially the same technical effect (equivalent features). Equivalent features include, for example, features that are variants that achieve substantially the same result in substantially the same way. Equivalent features include, for example, features that perform substantially the same function in substantially the same way to achieve substantially the same result.

[0113] Reference is made throughout this specification to various examples using adjectives or adjective phrases to describe features of the examples. Such description of a feature in connection with the examples indicates that the feature is present in some examples as described and present in other examples substantially as described.

[0114] While the foregoing specification has attempted to draw attention to features believed to be important, it should be understood that applicant may seek protection through claims directed to any patentable feature or combination of features described above and / or shown in the drawings, whether or not emphasized.

[0115] appendix: Further details are provided in the attached appendix, which is incorporated herein by reference.

[0116] JPEG2025531427000009.jpg92170 (Title: Resistivity of various human breast tissues) Abstract: In a clinical study conducted in collaboration with the Department of Pathology, a series of electrical impedance spectroscopy (EIS) experiments were conducted to extract the unique characteristics of benign and malignant human breast tissue in vitro. Digital images of the pathology slides were taken for each paraffin-sectioned breast tissue, allowing for the study of the effects of unwanted tissue types intermixed with the target tissue type. By recognizing intermixed tissues in the digital pathology slide images, the unwanted intermixed tissues could be removed from the EIS data, revealing the impedance patterns of target tissues, such as cancer cells. Initially, the impedance patterns of four tissue types were extracted: adipose tissue, connective tissue, ductal carcinoma (IDC), and fibroadenoma. The information in this data helps achieve the goal of being able to predict different diagnostic outcomes depending on various measurable life parameters for each patient, such as BMI and age. Keywords: Human breast tissue, Impedance, Electrical Impedance Spectroscopy, EIS, Breast Cancer. 1. Introduction Various types of breast tissue were isolated ex vivo from surgically excised breast tissue, and EIS data for each tissue type was digitally recorded before the tissues were paraffin-sectioned into pathology slides. Based on the impedance pattern characteristics of human tissues published by Cole and Cole (1941) and those published as guidance references by Jossinet (1998) and Wang (2001), EIS data were used to determine the impedance pattern characteristics of several tissue types within human breast tissue. One challenge in in vitro breast tissue EIS studies is the presence of different tissue types in the same biopsy or excision pathology sample, resulting in significant variability in the overall measured impedance. The approach employed was to analyze digital pathology slide images to identify the various tissue types present in the breast sample. The direction of current flow within the specimen during EIS measurements was also analyzed. The Cole-Cole impedance parameters of the various pure tissue types were used to distinguish between the various tissue types present within the same test pathology specimen. Next, an EIS dataset representing the various pure tissue types present throughout the tissue was extracted from the washed pure tissue specimen dataset examined at the cellular level. When the actual EIS impedance was compared to the simulated EIS impedance, the proportion of mixed tissues in the simulated data matched very well with the proportion found in the digital pathology slide. 2. Data Collection 2.1 Sample size Sixty-eight EIS datasets were obtained from the hospital, of which 31 were valid for analysis: 11 (35% of 31) were obtained from patients diagnosed with benign tumors, and 21 (68% of 31) were obtained from patients diagnosed with malignant tumors. Note that in some cases, both benign and malignant tissues were diagnosed. 2.2 Quality control of EIS specimens All datasets (by "dataset" we mean all EIS measurements sent to the pathology lab from all tissues taken from one patient in the operating room) must meet the following criteria to ensure their quality is sufficient to be "valid": There are two types of data comparisons: one that compares different types of tissue taken from the same patient, and one that compares the same type of tissue taken from different patients. Post-surgery timing EIS data should be collected within 15 minutes after surgery and the tissue should be stored in a temperature-controlled box (6°C). After this period, the electrical impedance characteristics of human tissue rapidly degrade. Background saline reference dataset An EIS background saline reference dataset (filling the test chamber with 1000 pS / cm saline) should be collected prior to EIS measurements on human tissue. The saline EIS dataset is used as a reference to calibrate the internal impedance differences of the EIS test unit. Tissue size relative to test chamber volume The tissue is cut to fit snugly into a cylindrical test chamber (5 mm diameter, 8 mm length). Saline is injected to ensure contact between the tissue and the electrodes at both ends of the test chamber. The size of the tissue sample must be equal to or greater than the volume of the chamber to avoid electrical currents caused by the tissue sample passing through the saline. The saline should not fill more than 10% of the chamber's volume; otherwise, the impedance pattern of the test tissue will be distorted, invalidating the impedance measurement of the sample. Data comparison There are two types of data comparisons: one comparing different tissue types from the same patient, and one comparing the same tissue type from different patients. 2.3 Digital pathology slide images The diagram below outlines the procedure used to capture digital pathology slide images of breast tissue at various fields of view (FOV). JPEG2025531427000010.jpg94170 2.4 Age and BMI distribution Thirty-one valid datasets were collected from 31 patients. Ages were as follows: <40 = 7, 40-60 = 14, ≥60 = 10, BMI <23 = 12, BMI 23-25 ​​= 10, BMI ≥25 = 9. Twenty-one patients were diagnosed with IDC tumors and 11 patients were diagnosed with fibroadenoma tumors. 3. Method 3.1 Procedure There are three steps in validating pure tissue impedance: i. classify tissues into "pure" and "mixed" groups according to pathology slide images (examples of mixed and pure tissues are shown in Figure 2); ii. determine impedance data and extract Cole-Cole parameters from the "pure" tissues as described in section 3.2; iii. use a patented cascade model (US patent to Wang); iv. remove the mixed tissue impedance component from the actual EIS data to generate "clean" data and converge the impedance to the "pure" tissue impedance. 3.2 Equation The Cole-Cole equation (Cole 1940, Cole and Cole 1941, McAdams and Jossinet 1995) describes the change in impedance at different frequencies. JPEG2025531427000011.jpg9170In formula, R ∞ and R0 = resistance at infinity or zero frequency F r = relaxation frequency a = relaxation time. If we consider the measurement object as a simplified cell suspension model with a three-element (RSC) electrical equivalent circuit (Fricke and Morse, 1925), the following equation can be used: JPEG2025531427000012.jpg10170In the formula, R = extracellular resistance, equal to R0 in (1). S=intracellular resistance R ∞ = Connect R and S in parallel. Four Cole-Cole parameters (R, S, F r , α) is calculated from the EIS bioimpedance curve using these two equations. 3.3 Integrated Model The figure below shows the integrated model consisting of Z1, Z2 and Z3 textures to calculate the simulated resistivity: JPEG2025531427000013.jpg30151where Z1 = resistivity of the target "pure" tissue Z2 and Z3 = resistivity of the "mixed" tissue in series and parallel with the target tissue. Values ​​vary depending on the pathology slide image. 4.Results 4.1 Digital pathology slide images of mixed and pure tissues The following pathology slide images show the connective tissue affected by the various components of adipose tissue: JPEG2025531427000014.jpg130150 4.2 Calculated Cole-Cole parameters for pure tissue Based on the selected EIS dataset, the impedance curves of four "pure" tissues (adipose, connective tissue, IDC, fibroadenoma) are calculated and plotted as follows: JPEG2025531427000015.jpg84152 Next, the Cole-Cole parameters for each type of tissue are calculated and listed as follows: JPEG2025531427000016.jpg49170 Table 2 shows the Cole-Cole parameters for the impedance of freshly excised human breast tissue as published in Jossinet's paper. JPEG2025531427000017.jpg48170 4.3 Comparison of simulation and actual measurements Below are examples of actual measured and simulated EIS impedance (real) data: JPEG2025531427000018.jpg83139 4.4 Standard deviation after purification Next, the Cole-Cole parameters for each type of tissue are calculated and listed as follows: JPEG2025531427000019.jpg67170 5. Discussion The R and S calculated from the Shanghai connective tissue dataset drop from over 20 Ω·m in the Josinet dataset to less than 4 Ω·m, which may be due to the presence of adipose tissue within the measured connective tissue. The relaxation frequencies of connective, adipose, and fibroadenoma tissues in the Shanghai dataset are much higher than those in the Josinet dataset. At this time, more data are needed to validate these new findings. However, IDC is the tissue type that most closely resembles the Cole-Cole parameters extracted from the EIS dataset. 6. Conclusion The presented study is essential before diagnostic use of electrical impedance mammography (EIM) images, because each pixel in a 3D EIM image is mixed with surrounding tissue, and the impedance characteristics of the mixed tissue must be identified with solid evidence using pathology slide images and EIS data of the same tissue. Acknowledgments We would like to express our gratitude to the Shanghai International Peace Maternal Health Hospital of China Welfare Institute for providing all the data and the opportunity to conduct the clinical trial during the difficult time of the COVID-19 pandemic. References Cole K 1940 Permeability and impermeability of cell membranes for ions Cold Spring Harbor Symp., Quant. Biol. 8 110-22 Cole K and Cole R 1941 Dispersion and absorption in dielectrics J. of Chem. Phys. 5 341-350 Jossinet J 1998 The impedivity of freshly excised human breast tissue Physiol. Meas. 19 61-75 McAdams E and Jossinet J 1995 Tissue impedance: a historical overview Physiol. Meas. 16 (suppl) A1-A13 Wang W, et al 2001 Preliminary results from an EIT breast imaging simulation system Physiol. Meas 22(1) 39-48 Wang W, 2005.02 Apparatus and method for detecting abnormalities in bodily matter US6856842(B1)

Claims

1. A method for virtually refining experimental electrical impedance signals for a target tissue using a non-target tissue model.

2. The method of claim 1 , wherein the target tissue is a tumor tissue.

3. obtaining a refined impedance transfer function based on an electrical impedance model that includes one or more non-target tissue components and does not include the target tissue component; Refining the experimental electrical impedance signal using the refined impedance transfer function to obtain a virtually refined signal of the tissue component of interest. A method comprising:

4. 4. The method of claim 3, further comprising: refining an experimental electrical impedance signal using the refined impedance transfer function; and assuming an electrical impedance model including the target tissue component and the one or more non-target tissue components to obtain a virtually refined signal of the target tissue component, wherein the target tissue is tumor tissue and the non-target tissue is non-tumor tissue.

5. Accessing non-tumor tissue dependent impedance transfer functions as a function of age and / or obesity indicators; The obtained impedance transfer function is used to correct the experimental electrical impedance signal to obtain a signal that is less dependent on non-tumor tissue and more dependent on the impedance of tumor tissue. A method comprising:

6. The method of claim 5 , wherein the indicator of obesity is body mass index.

7. The method of any one of claims 1 to 6, wherein the non-tumor tissue impedance is based on connective tissue impedance and adipose tissue impedance.

8. The method of any one of claims 1 to 7, wherein the tumor tissue impedance is benign, such as fibroadenoma tissue impedance, or cancerous, such as IDC tissue impedance.

9. The method according to any one of claims 1 to 8, wherein the experimental signal is an electrical impedance signal from an in vitro measurement.

10. The method according to any one of claims 1 to 8, wherein the experimental signal is an electrical impedance signal from an in-vivo measurement.

11. The method according to any one of claims 1 to 10, wherein the electrical impedance signal is used for electrical impedance imaging.

12. The method according to any one of claims 1 to 11, wherein the electrical impedance model is a mixed impedance model including a target tissue impedance (Z1) and non-target tissue impedances (Z2, Z3).

13. 13. The method of claim 12, wherein the target tissue impedance Z1 and the first non-target tissue impedance Z3 are electrically in series as a combination, and the combination is electrically in parallel with the second non-target tissue impedance Z2.

14. 14. The method of claim 13, wherein the target tissue impedance Z1 is either benign, such as a fibroadenoma impedance, or cancer, such as an IDC impedance, depending on the desired target; the first non-target tissue impedance Z3 is either a connective tissue impedance or an adipose tissue impedance; and the second non-target tissue impedance Z2 is either a connective tissue impedance or an adipose tissue impedance.

15. 15. The method of any one of claims 1 to 14, comprising determining the impedance (Z1) of the target tissue using an in vitro sample and / or determining the impedance of the non-target tissue using an in vitro sample, and optionally determining the impedance Z3 of the first non-target tissue using an in vitro sample and / or determining the impedance Z2 of the second non-target tissue using an in vitro sample.

16. The method of any one of claims 1 to 15, wherein the electrical impedance model is determined to best fit in vitro data of one or more samples.

17. 17. The method of any one of claims 13 to 16, wherein the relative ratio of the first non-target tissue impedance Z3 to the second non-target tissue impedance in the electrical impedance model is determined by optimization (best fit), and the target tissue impedance (Z1) is known or obtained from a refined tissue database determined and calibrated from pathologically identified cell-based tissue properties.

18. 18. The method of any one of claims 1 to 17, wherein the electrical impedance model is converted to a non-target model for non-target tissue by setting the impedance (Z1) of the target tissue to zero in the electrical impedance model.

19. 19. The method according to any one of claims 1 to 18, wherein the non-target tissue models (Z2, Z3) are used to estimate non-target components, which are removed from the experimental electrical impedance signal to estimate a target signal, the removal being performed in the time domain or the frequency domain.

20. 20. The method of any one of claims 1 to 19, wherein the non-target tissue model (Z2, Z3) is used to estimate a non-target signal, which is subtracted from an experimental signal to estimate a target signal.

21. 21. The method of claim 20, comprising analyzing the in vivo target signal to identify and locate changes in spatial electrical impedance representative of tumor tissue.

22. 21. The method of claim 20, comprising imaging the tumor tissue.

23. Apparatus comprising means for carrying out the method according to any one of claims 1 to 22.

24. A computer program comprising computer code, the computer code being executed by one or more processors to perform the method of any one of claims 1 to 22.

25. An imaging system comprising means for carrying out the method according to any one of claims 1 to 22.

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